Centre for Statistical & Survey Methodology Working Paper Series

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This paper considers parameter estimation in multivariate heteroscedastic models with unspecific correlation. In this paper, we propose an asymptotic quasi-likelihood (AQL) approach which utilises a nonparametric kernel estimator of variance covariances matrix ∑ to replace the true ∑ in the standard quasi-likelihood. The kernel estimation avoids the risk of potential missspecification of ∑ and thus make the parameter estimator more robust. The well developed theory framework for AQL (See Lin, 2000) provides a solid base for ensuring the efficiency of the approach developed in this paper. This has been further verified by empirical studies carried out in this paper.